This application, and the innovations and related subject matter disclosed herein, (collectively referred to as the “disclosure”) generally concern systems for detecting and removing unwanted noise in an observed signal, and associated techniques. More particularly but not exclusively, disclosed systems and associated techniques can detect undesirable audio noise in an observed audio signal and remove the unwanted noise in an imperceptible or suitably imperceptible manner. As but one example, disclosed systems and techniques can detect and remove unwanted “clicks” arising from manual activation of an actuator (e.g., one or more keyboard strokes, or mouse clicks) or emitted by a speaker transducer to mimic activation of such an actuator. Some disclosed systems are suitable for removing unwanted noise from a recorded signal, a live signal (e.g., telephony, video and/or audio simulcast of a live event), or both. Disclosed systems and techniques can be suitable for removing unwanted noise from signals other than audio signals, as well.
By way of illustration, clicking a button or a mouse might occur when a user records a video or attends a telephone conference. Such interactions can leave an audible “click” or other undesirable artifact in the audio of the video or telephone conference. Such artifacts can be subtle (e.g., have a low artifact-signal-to-desired-signal ratio), yet perceptible, in a forgiving listening environment.
Solving such a problem involves two different aspects: (1) target-signal detection; and (2) target-signal removal. Detection of a target signal, sometimes referred to in the art as “signal localization” addresses two primary issues: (1) whether a target signal is present; and (2) if so, when it occurred. With a known target signal and only additive white noise, a matched filter is optimal and can efficiently be computed for all partitions using known FFT techniques. The matched filter can be used to remove the target signal.
However, previously known detectors, e.g., based on matched filters, generally are unsuitable for use in real-world applications where target signals are unknown and can vary. For example, the presence of a noise (or “target”) signal within an observed signal cannot be guaranteed. Moreover, a noise signal can vary among different frequencies, and a target signal can emphasize one or more frequency bands. Still further, some target signals have a primary component and one or more secondary components.
Thus, a need remains for computationally efficient systems and associated techniques to detect unwanted noise signals in real-world applications, where the presence or absence of a target signal is not known, and where target signals can vary. As well, a need remains for computationally efficient systems and techniques to remove unwanted noise from an observed signal in a manner that suitably obscures the removal processing from a user's perception. Ideally, such systems and techniques will be suitable for removing a variety of classes of target signals (e.g., mouse clicks, keyboard clicks, hands clapping) from a variety of classes of observed signals (e.g., speech, music, environmental background sounds, street noise, café noise, and combinations thereof).